scholarly journals On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Elisabeth K¨allstr¨om ◽  
Tomas Olsson ◽  
John Lindstr¨om ◽  
Lars Hakansson ◽  
Jonas Larsson
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Extraction Methods ◽  
Continuous Queries ◽  
Case Based Reasoning ◽  
Heavy Duty ◽  
Data Stream Management ◽  
Detection And Diagnosis ◽  
Case Based ◽  
Slippage Detection

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machine in combination with feature extraction and classification methods may be utilized.This paper presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components, the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features, which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation.

scholarly journals Automatic Pancreatic Ductal Adenocarcinoma Detection in Whole Slide Images Using Deep Convolutional Neural Networks

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Fu ◽  
Weiming Mi ◽  
Boju Pan ◽  
Yucheng Guo ◽  
Junjie Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Network Architecture ◽  
Extraction Methods ◽  
Ductal Adenocarcinoma ◽  
Deep Convolutional Neural Networks ◽  
Histopathological Images ◽  
Feature Based ◽  
Detection And Diagnosis ◽  
Histopathology Image Analysis

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancer types worldwide, with the lowest 5-year survival rate among all kinds of cancers. Histopathology image analysis is considered a gold standard for PDAC detection and diagnosis. However, the manual diagnosis used in current clinical practice is a tedious and time-consuming task and diagnosis concordance can be low. With the development of digital imaging and machine learning, several scholars have proposed PDAC analysis approaches based on feature extraction methods that rely on field knowledge. However, feature-based classification methods are applicable only to a specific problem and lack versatility, so that the deep-learning method is becoming a vital alternative to feature extraction. This paper proposes the first deep convolutional neural network architecture for classifying and segmenting pancreatic histopathological images on a relatively large WSI dataset. Our automatic patch-level approach achieved 95.3% classification accuracy and the WSI-level approach achieved 100%. Additionally, we visualized the classification and segmentation outcomes of histopathological images to determine which areas of an image are more important for PDAC identification. Experimental results demonstrate that our proposed model can effectively diagnose PDAC using histopathological images, which illustrates the potential of this practical application.

scholarly journals Health Monitoring for Elderly: An Application Using Case-Based Reasoning and Cluster Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Mobyen Uddin Ahmed ◽  
Hadi Banaee ◽  
Amy Loutfi
Keyword(s):  
Oxygen Saturation ◽  
Pulse Rate ◽  
Health Monitoring ◽  
Pulse Oximeter ◽  
Extraction Methods ◽  
Case Based Reasoning ◽  
Blood Oxygen Saturation ◽  
Time Frequency ◽  
And Cluster Analysis ◽  
Case Based

This paper presents a framework to process and analyze data from a pulse oximeter which remotely measures pulse rate and blood oxygen saturation from a set of individuals. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to their similarity. Record collection has been performed using a personalized health profiling approach in which participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction methods in time, frequency, and time-frequency domains, as well as data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates an alarm according to the case outcomes. The system has been compared with an expert's classification, and a 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements, the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in the analysis of continuous health monitoring and can be used as a suitable method in home/remote monitoring systems.

A Recent Study on High Dimensional Features Used in Stego Image Anomaly Detection

2018 ◽  
pp. 49-66
Author(s):  
Hemalatha J ◽  
KavithaDevi M.K. ◽  
Geetha S.
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
High Dimension ◽  
Digital Images ◽  
Extraction Methods ◽  
High Dimensional ◽  
Extraction Techniques ◽  
Stego Image ◽  
Feature Spaces ◽  
Higher Dimensional

This chapter describes how ample feature extraction techniques are available for detecting hidden messages in digital images. In the recent years, higher dimensional features are extracted to detect the complex and advanced steganographic algorithms. To improve the precision of steganalysis, many combinations of high dimension feature spaces are used by recent steganalyzers. In this chapter, we present a summary of several methods existing in literature. The aim is to provide a broad introduction to high dimensional features space used so for and to state which the most accurate and best feature extraction methods is.

scholarly journals COVID19: A Natural Language Processing and Ontology Oriented Temporal Case-Based Framework for Early Detection and Diagnosis of Novel Coronavirus

2020 ◽  
Author(s):  
Olaide N. Oyelade ◽  
Absalom E. Ezugwu
Keyword(s):  
Early Detection ◽  
Language Processing ◽  
World Health ◽  
Initial Population ◽  
Case Based Reasoning ◽  
Specific Patient ◽  
The World ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis ◽  
Case Based

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

Gas Turbine Fault Detection and Diagnosis Using Nonlinear Feature Extraction Methods

2005 ◽  
Author(s):  
Joydeb Mukherjee ◽  
Venkataramana B. Kini ◽  
Sunil Menon ◽  
Lalitha Eswara
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Gas Turbine ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Extraction Methods ◽  
Fault Detection And Diagnosis ◽  
Sensor Data ◽  
Detection And Diagnosis

Accurate gas turbine fault detection and diagnosis (FDD) is essential to improving airline safety as well as in reducing airline costs associated with delays and cancellations. In this paper, we present FDD methods based on feature extraction methods using nonlinear principal component analysis (NLPCA) and curvilinear component analysis (CCA). The underlying principle of both methods is to find the most representative feature space corresponding to gas turbine normal and faulty operations. During operation, new sensor data is located in this feature space and then it is determined whether a particular fault is indicated. NLPCA is an extension of linear PCA methods to the nonlinear domain; therefore, it is intrinsically better suited to nonlinear domains such as the gas turbine engine. The CCA method is another approach to clustering having superior properties for determining cluster manifolds automatically compared to the popular selforganizing map (SOM) method of clustering. The developed methods are tested with snapshot data collected at takeoff, both normal and faulty, from a turbofan gas turbine propulsion engine and the results are presented.

scholarly journals COVID19: A Natural Language Processing and Ontology Oriented Temporal Case-Based Framework for Early Detection and Diagnosis of Novel Coronavirus

2020 ◽  
Author(s):  
Olaide Nathaniel Oyelade ◽  
Absalom E. Ezugwu
Keyword(s):  
Natural Language Processing ◽  
Early Detection ◽  
Natural Language ◽  
Language Processing ◽  
World Health ◽  
Case Based Reasoning ◽  
The World ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis ◽  
Case Based

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to interpret and solve new cases even at their early stage to the advanced stage. The approach adopted in this study employs a natural language processing (NLP) technique to parse records of cases and thereafter formalize each case which is represented as a mini-ontology file. The formalized case is therefore parsed into a CBR model to allow for classification of the case into positive or negative to COVID-19. Meanwhile, feature extraction for each case is done by classifying tokens extracted by the NLP approach into special, temporal and thematic classes before encoding them using an ontology modeling method. The CBR model therefore leverages on the formalized features to compute the similarity of the new case with extracted similar cases from the archive of the CBR model. The proposed framework was populated with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach leverages on locations (spatial) and time (temporal) of contagion to successfully detect cases even in their early stages of two days onward before the incubation period of fourteen days. The proposed framework achieved an accuracy of 97.10%, sensitivity of 0.98 and specificity of .066. The study found that the proposed model can assist physicians to easily diagnose and isolate cases, thereby minimizing the rate of contagion and reducing false diagnosis as observed in some parts of the globe.

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